In this work, we use measure theory to provide new sufficient conditions for the existence, uniqueness and global exponential stability of piecewise pseudo almost periodic solutions of neutral-type inertial neural networks with mixed delay and impulsive perturbations. By proposing a variable substitution, the neutral-type inertial neural networks can be rewritten as a first-order differential equation. As a consequence, we investigate the exponential attractiveness of \(\mu \)-piecewise pseudo almost periodic solutions of the considered model. Moreover, some numerical simulations are given to illustrate our main results.

中文翻译：

---

$$\mu $$ μ - 中性型惯性神经网络模型的分段伪几乎周期解的动力学：存在性和吸引力

在这项工作中，我们使用测度论为具有混合延迟和脉冲扰动的中性型惯性神经网络的分段伪几乎周期解的存在性、唯一性和全局指数稳定性提供了新的充分条件。通过提出变量替换，中性型惯性神经网络可以改写为一阶微分方程。因此，我们研究了\(\mu \) -所考虑模型的分段伪几乎周期解的指数吸引力。此外，还给出了一些数值模拟来说明我们的主要结果。